Title

Authors

Keywords

web documents, extractive summarization, text classification

Abstract

Text classification categorizes Web documents in large collections into predefined classes based on their contents. Unfortunately, the classification process can be time-consuming and users are still required to spend considerable amount of time scanning through the classified Web documents to identify the ones that satisfy their information needs. In solving this problem, we first introduce CorSum, an extractive single-document summarization approach, which is simple and effective in performing the summarization task, since it only relies on word similarity to generate high-quality summaries. Hereafter, we train a Naïve Bayes classifier on CorSum-generated summaries and verify the classification accuracy using the summaries and the speed-up during the process. Experimental results on the DUC-2002 and 20 Newsgroups datasets show that CorSum outperforms other extractive summarization methods, and classification time is significantly reduced using CorSum-generated summaries with compatible accuracy. More importantly, browsing summaries, instead of entire documents, classified to topic-oriented categories facilitates the information searching process on the Web.